Predicting the future behaviour of people remains an open challenge for the development of risk-aware autonomous vehicles. An important aspect of this challenge is effectively capturing the uncertainty which is inherent to human behaviour. This paper studies an approach for probabilistic motion forecasting with improved accuracy in the predicted sample likelihoods. We are able to learn multi-modal distributions over the motions of an agent solely from data, while also being able to provide predictions in real-time. Our approach achieves state-of-the-art results on the inD dataset when evaluated with the standard metrics employed for motion forecasting. Furthermore, our approach also achieves state-of-the-art results when evaluated with respect to the likelihoods it assigns to its generated trajectories. Evaluations on artificial datasets indicate that the distributions learned by our model closely correspond to the true distributions observed in data and are not as prone towards being over-confident in a single outcome in the face of uncertainty.
翻译:预测人的未来行为对于开发风险感知自主车辆仍然是一个开放性挑战。有效地捕捉人类行为固有的不确定性是这一挑战的重要方面。本文研究了一种概率运动预测方法,可以提高预测样本的准确性。我们能够仅通过数据学习代理的运动的多模态分布,同时也能够实时提供预测的结果。当使用运动预测的标准指标进行评估时,我们的方法在 inD 数据集上实现了最先进的结果。此外,当评估我们的方法分配给生成轨迹的概率时,我们的方法也实现了最先进的结果。在人造数据集上的评估表明,我们的模型学习的分布与数据中观察到的真实分布密切相符,并不容易在面对不确定性时过于自信,只给出单一的结果。